# search.py
# ---------
# Licensing Information:  You are free to use or extend these projects for
# educational purposes provided that (1) you do not distribute or publish
# solutions, (2) you retain this notice, and (3) you provide clear
# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
# 
# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
# The core projects and autograders were primarily created by John DeNero
# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and
# Pieter Abbeel (pabbeel@cs.berkeley.edu).


"""
In search.py, you will implement generic search algorithms which are called by
Pacman agents (in searchAgents.py).
"""

import util

class SearchProblem:
    """
    This class outlines the structure of a search problem, but doesn't implement
    any of the methods (in object-oriented terminology: an abstract class).

    You do not need to change anything in this class, ever.
    """

    def getStartState(self):
        """
        Returns the start state for the search problem.
        """
        util.raiseNotDefined()

    def isGoalState(self, state):
        """
          state: Search state

        Returns True if and only if the state is a valid goal state.
        """
        util.raiseNotDefined()

    def getSuccessors(self, state):
        """
          state: Search state

        For a given state, this should return a list of triples, (successor,
        action, stepCost), where 'successor' is a successor to the current
        state, 'action' is the action required to get there, and 'stepCost' is
        the incremental cost of expanding to that successor.
        """
        util.raiseNotDefined()

    def getCostOfActions(self, actions):
        """
         actions: A list of actions to take

        This method returns the total cost of a particular sequence of actions.
        The sequence must be composed of legal moves.
        """
        util.raiseNotDefined()


def tinyMazeSearch(problem):
    """
    Returns a sequence of moves that solves tinyMaze.  For any other maze, the
    sequence of moves will be incorrect, so only use this for tinyMaze.
    """
    from game import Directions
    s = Directions.SOUTH
    w = Directions.WEST
    return  [s, s, w, s, w, w, s, w]

def depthFirstSearch(problem: SearchProblem):
    """
    Search the deepest nodes in the search tree first.

    Your search algorithm needs to return a list of actions that reaches the
    goal. Make sure to implement a graph search algorithm.

    To get started, you might want to try some of these simple commands to
    understand the search problem that is being passed in:

    print("Start:", problem.getStartState())
    print("Is the start a goal?", problem.isGoalState(problem.getStartState()))
    print("Start's successors:", problem.getSuccessors(problem.getStartState()))
    """
    "*** YOUR CODE HERE ***"
    from util import Stack
    # 深度搜索选择栈
    dfs_stack= Stack()
    #记录遍历过的节点
    closed =[]
    # 加入起始状态节点
    dfs_stack.push((problem.getStartState(), []))
    #如果主栈不为空，则循环搜索          
    while not dfs_stack.isEmpty():
        # 取出栈顶元素并删掉,path 记录路径
        #print("-----------------------------")
        #print("操作前栈的的大小",dfs_stack.lenl())
        cur_node,path=dfs_stack.pop()
        #print("pop操作后栈的的大小",dfs_stack.lenl())
        #print("记录路径",path)
        #print(dfs_stack.lenl()) 
        # 如果当前节点到达目标位置
        if problem.isGoalState(cur_node):
            return path
        if cur_node in closed:
            continue
        # 当前节点加入到走过路程中
        closed.append(cur_node)
        for next_node, direction, cost in problem.getSuccessors(cur_node):
            # 如果后继节点不在走过路程中，就将后继节点的为置和当前节点路径加上此次方向
            if (next_node not in closed):
                #print("下一步的方向",direction)
                dfs_stack.push((next_node ,path + [direction]))
    util.raiseNotDefined()

def breadthFirstSearch(problem: SearchProblem):
    """Search the shallowest nodes in the search tree first."""
    "*** YOUR CODE HERE ***"
    #广度搜索选择队列
    from util import Queue
    queue=Queue()
    closed=[]
    queue.push((problem.getStartState(),[]))
    while not queue.isEmpty():
        cur_node,path=queue.pop()
        if problem.isGoalState(cur_node):
            return path
        if  cur_node in closed:
            continue
        closed.append(cur_node)
        for next_node in problem.getSuccessors(cur_node):
            if (next_node[0] not in closed):
                queue.push((next_node[0] ,path + [next_node[1]]))
    util.raiseNotDefined()
def uniformCostSearch(problem: SearchProblem):
    """Search the node of least total cost first."""
    "*** YOUR CODE HERE ***"
    from util import PriorityQueue
    #创一个优先级队列
    p_queue=PriorityQueue()
    closed=[]
    p_queue.push((problem.getStartState(),[]), 0)
    while not p_queue.isEmpty():
        cur_node,path=p_queue.pop()
        if problem.isGoalState(cur_node):
            return path
        if cur_node in closed:
            continue
        closed.append(cur_node)
        for next_node in problem.getSuccessors(cur_node):
            if next_node[0] not in closed:
                actions=path+[next_node[1]]
                priority=problem.getCostOfActions(actions)

                p_queue.update((next_node[0],actions), priority)
        
    util.raiseNotDefined()

def nullHeuristic(state, problem=None):
    """
    A heuristic function estimates the cost from the current state to the nearest
    goal in the provided SearchProblem.  This heuristic is trivial.
    """
    return 0

def aStarSearch(problem: SearchProblem, heuristic=nullHeuristic):
    """Search the node that has the lowest combined cost and heuristic first."""
    "*** YOUR CODE HERE ***"
    from util import PriorityQueue
    #创一个优先级队列
    p_queue=PriorityQueue()
    closed=[]
    state=problem.getStartState()
    p_queue.push((problem.getStartState(),[]),nullHeuristic(state,problem))
    
    while not p_queue.isEmpty():
        cur_node,path=p_queue.pop()
        if problem.isGoalState(cur_node):
            return path
        if cur_node in closed:
            continue
        closed.append(cur_node)
        for next_node in problem.getSuccessors(cur_node):
            if next_node[0] not in closed:
                actions=path+[next_node[1]]
                priority=problem.getCostOfActions(actions)
                allCost=priority+heuristic(next_node[0],problem)
                p_queue.update((next_node[0],actions), allCost)
    util.raiseNotDefined()


# Abbreviations
bfs = breadthFirstSearch
dfs = depthFirstSearch
astar = aStarSearch
ucs = uniformCostSearch